AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

Designing 2-stage recycling operations for increased usage of undervalued raw materials

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Materials Science and Engineering, 2015.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 126-130).Recycling provides a key strategy to move towards a more sustainable society by partially mitigating the impact of fast-growing material consumption. Recent advances in reprocessing technologies enable recyclers to incorporate low-quality secondary materials into higher quality finished products. Despite technological development, the use of these materials in the re-melting stage to produce final alloys is still limited. This thesis addresses this issue by raising the following question: given the complexity of the reprocessing operational environment, what is the most effective way to manage two-stage recycling operations to maximize the usage of low-quality secondary materials? This thesis answers this question for two systems: when outputs from the reprocessing stage can be delivered (1) as sows and (2) as liquid metals to the re-melting stage. In the first system, the main barrier to use of these materials is the highly variable quality of raw materials. This study suggests the use of data mining as a strategy to manage raw materials with uncertain quality using existing data from the recycling industry. A clustering analysis provides criteria for grouping raw materials by recognizing the pattern of varied compositions. This grouping (binning) strategy using the clustering analysis increases the homogeneity and distinctiveness of uncertain raw materials, allowing recyclers to increase their usage while maintaining minimum information about them. In the second system, significant energy cost can be saved by immediately incorporating reprocessed secondary raw materials as liquid metal into final alloy production. In this case, the coordination between the reprocessing stage and the re-melting stage is critical. This study suggests integrated production planning for two stages. The mathematical pooling problem is used to model two-stage recycling operations. Integrated planning across the two operations can adjust batch plans and design intermediate products by reflecting demand information of final products. This approach maximizes the use of intermediate products as liquid in the remelting stage and, therefore, lowers energy cost significantly. Both strategies are applied to industrial cases of aluminum recycling to explore the benefits and limitations. The results indicate the potential opportunity to significantly reduce material costs and to increase the use of undervalued secondary raw materials.by Jiyoun Christina Chang.Ph. D

Data Mining Toward Increased Use of Aluminum Dross

Recycling provides a key strategy to move toward a more sustainable society by partially mitigating the impact of fast-growing material consumption. One main barrier to increased recycling arises from the fact that in many real world contexts, the quality of secondary (or scrap) material is unknown and highly variable. Even if scrap material is of known quality, there may be finite space or limited operational flexibility to separate or sort these materials prior to use. These issues around identification and grouping given the operational constraints create limitations to simply developing an appropriate sorting strategy, let alone implementing one. This study suggests the use of data mining as a strategy to manage raw materials with uncertain quality using existing data from the recycling industry. A clustering analysis is used to recognize the pattern of raw materials across a broad compositional range in order to provide criteria for grouping (binning) raw materials. This strategy is applied to an industrial case of aluminum recycling to explore the benefits and limitations in terms of secondary material usage. In particular, the case investigated is around recycling industrial byproducts (termed dross for the case of the aluminum industry). The binning strategy obtained by the clustering analysis can significantly reduce material cost by increasing the compositional homogeneity and distinctiveness of uncertain raw materials. This result suggests the potential opportunity to increase low-quality secondary raw material usage before investment in expensive sorting technology.Norsk Hydr

Integrated planning for design and production in two-stage recycling operations

Recycling is a key strategy to reduce the environmental impact associated with industrial resource use. Recent improvements in materials recovery technologies offer the possibility for recouping additional value from recycling. However, incorporation of secondary raw materials into production may be constrained by operational complexity in two-stage blending processes. In this paper, we derive an analytical solution to demonstrate the importance of integrated planning (IP) approaches for two-stage blending operations in recycling. Our results suggest that the quality of materials obtained from the first stage strongly influences performance in the second stage. Current disjointed planning (DP) approaches in the recycling industry, where individual stages are independently planned without decision-making about intermediate blend design, overlook this interaction and, therefore, make conservative use of lower quality materials. We develop an IP model using a formulation of the pooling problem and apply it to an industrial-scale aluminum recycling facility located in Europe. The results suggest that the IP model can reduce material costs by more than 5%, for the case examined, and can enable increased use of undervalued raw materials. This study also investigates the impact of variations in operational conditions on the benefits of IP. Keywords: Production; Material recycling; Integrated planning; Two-stage blending process (pooling problem); Design of intermediate productsNational Science Foundation (U.S.) (Award 1605050)Fundação para a Ciência e a Tecnologia (Project MITP-TB/PFM/0005/2013